Overview

Dataset statistics

Number of variables26
Number of observations1948424
Missing cells142
Missing cells (%)< 0.1%
Duplicate rows113
Duplicate rows (%)< 0.1%
Total size in memory401.4 MiB
Average record size in memory216.0 B

Variable types

Categorical8
DateTime2
Numeric16

Alerts

Dataset has 113 (< 0.1%) duplicate rowsDuplicates
VIN has a high cardinality: 1797664 distinct valuesHigh cardinality
MotorType has a high cardinality: 32758 distinct valuesHigh cardinality
Make has a high cardinality: 796 distinct valuesHigh cardinality
Model has a high cardinality: 15540 distinct valuesHigh cardinality
Type is highly imbalanced (78.1%)Imbalance
Make is highly imbalanced (55.8%)Imbalance
VehicleType is highly imbalanced (63.5%)Imbalance
VehicleClass is highly imbalanced (79.3%)Imbalance
Result is highly imbalanced (64.8%)Imbalance
Defects9 is highly skewed (γ1 = 41.12623739)Skewed
VIN is uniformly distributedUniform
DefectsA has 63395 (3.3%) zerosZeros
DefectsB has 1737482 (89.2%) zerosZeros
DefectsC has 1927545 (98.9%) zerosZeros
Defects0 has 1727980 (88.7%) zerosZeros
Defects1 has 900069 (46.2%) zerosZeros
Defects2 has 1645046 (84.4%) zerosZeros
Defects3 has 1647310 (84.5%) zerosZeros
Defects4 has 1117227 (57.3%) zerosZeros
Defects5 has 1377189 (70.7%) zerosZeros
Defects6 has 371575 (19.1%) zerosZeros
Defects7 has 1926885 (98.9%) zerosZeros
Defects8 has 1911897 (98.1%) zerosZeros
Defects9 has 1946229 (99.9%) zerosZeros

Reproduction

Analysis started2023-04-17 14:11:30.581286
Analysis finished2023-04-17 14:12:59.296944
Duration1 minute and 28.72 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Type
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.7 MiB
pravidelná
1681501 
opakovaná
 
124298
před registrací
 
91672
evidenční
 
39279
na žádost zákazníka
 
7304
Other values (8)
 
4370

Length

Max length37
Median length10
Mean length10.221125
Min length3

Characters and Unicode

Total characters19915085
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowevidenční
2nd rowpravidelná
3rd rowpravidelná
4th rowpravidelná
5th rowpravidelná

Common Values

ValueCountFrequency (%)
pravidelná 1681501
86.3%
opakovaná 124298
 
6.4%
před registrací 91672
 
4.7%
evidenční 39279
 
2.0%
na žádost zákazníka 7304
 
0.4%
před registrací - opakovaná 2988
 
0.2%
před schválením tech. zp. 894
 
< 0.1%
silniční - opakovaná po DN 260
 
< 0.1%
silniční - opakovaná 89
 
< 0.1%
ADR 58
 
< 0.1%
Other values (3) 81
 
< 0.1%

Length

2023-04-17T16:12:59.337963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pravidelná 1681501
81.3%
opakovaná 127693
 
6.2%
před 95610
 
4.6%
registrací 94660
 
4.6%
evidenční 39279
 
1.9%
na 7304
 
0.4%
žádost 7304
 
0.4%
zákazníka 7304
 
0.4%
3395
 
0.2%
schválením 950
 
< 0.1%
Other values (7) 2852
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 2053482
10.3%
e 1952252
9.8%
p 1906014
9.6%
n 1904054
9.6%
r 1870821
9.4%
v 1849423
9.3%
á 1824775
9.2%
d 1823694
9.2%
i 1816138
9.1%
l 1682800
8.4%
Other values (20) 1231632
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19789662
99.4%
Space Separator 119428
 
0.6%
Dash Punctuation 3395
 
< 0.1%
Other Punctuation 1900
 
< 0.1%
Uppercase Letter 700
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2053482
10.4%
e 1952252
9.9%
p 1906014
9.6%
n 1904054
9.6%
r 1870821
9.5%
v 1849423
9.3%
á 1824775
9.2%
d 1823694
9.2%
i 1816138
9.2%
l 1682800
8.5%
Other values (13) 1106209
5.6%
Uppercase Letter
ValueCountFrequency (%)
D 320
45.7%
N 260
37.1%
A 60
 
8.6%
R 60
 
8.6%
Space Separator
ValueCountFrequency (%)
119428
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3395
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1900
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19790362
99.4%
Common 124723
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2053482
10.4%
e 1952252
9.9%
p 1906014
9.6%
n 1904054
9.6%
r 1870821
9.5%
v 1849423
9.3%
á 1824775
9.2%
d 1823694
9.2%
i 1816138
9.2%
l 1682800
8.5%
Other values (17) 1106909
5.6%
Common
ValueCountFrequency (%)
119428
95.8%
- 3395
 
2.7%
. 1900
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17805180
89.4%
None 2109905
 
10.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2053482
11.5%
e 1952252
11.0%
p 1906014
10.7%
n 1904054
10.7%
r 1870821
10.5%
v 1849423
10.4%
d 1823694
10.2%
i 1816138
10.2%
l 1682800
9.5%
o 262950
 
1.5%
Other values (15) 683552
 
3.8%
None
ValueCountFrequency (%)
á 1824775
86.5%
í 142565
 
6.8%
ř 95633
 
4.5%
č 39628
 
1.9%
ž 7304
 
0.3%

VIN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1797664
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size29.7 MiB
ZCFC435D305223238
 
7
WMAN38ZZ6MY419431
 
7
VF1JLBHA67V282178
 
6
WMAH20ZZZ2W044816
 
6
VF1BG0M0524325728
 
6
Other values (1797659)
1948392 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters33123208
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1654351 ?
Unique (%)84.9%

Sample

1st rowZFA25000002617401
2nd rowTAH31100EF1112050
3rd rowTN9FV1Z00RAAM5032
4th rowTN9FS1Z00RAAM5122
5th rowZAR93700005018667

Common Values

ValueCountFrequency (%)
ZCFC435D305223238 7
 
< 0.1%
WMAN38ZZ6MY419431 7
 
< 0.1%
VF1JLBHA67V282178 6
 
< 0.1%
WMAH20ZZZ2W044816 6
 
< 0.1%
VF1BG0M0524325728 6
 
< 0.1%
TMBVCB5J775017515 5
 
< 0.1%
TSMMAB44S00926711 5
 
< 0.1%
KMFYKN7HP4U054463 5
 
< 0.1%
WAUZZZ8E87A102231 5
 
< 0.1%
WDB9340621L324127 5
 
< 0.1%
Other values (1797654) 1948367
> 99.9%

Length

2023-04-17T16:12:59.456632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zcfc435d305223238 7
 
< 0.1%
wman38zz6my419431 7
 
< 0.1%
vf1jlbha67v282178 6
 
< 0.1%
wmah20zzz2w044816 6
 
< 0.1%
vf1bg0m0524325728 6
 
< 0.1%
vant36zzzyl024238 5
 
< 0.1%
yv1vw70823f955950 5
 
< 0.1%
tmbjb16y813174531 5
 
< 0.1%
vf1vbu4z251855846 5
 
< 0.1%
yv2a4cfa45b392492 5
 
< 0.1%
Other values (1797654) 1948367
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 3020728
 
9.1%
1 2597647
 
7.8%
2 2126799
 
6.4%
3 1973446
 
6.0%
6 1762506
 
5.3%
4 1733414
 
5.2%
5 1716567
 
5.2%
7 1556241
 
4.7%
Z 1540391
 
4.7%
8 1463087
 
4.4%
Other values (29) 13632382
41.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19272809
58.2%
Uppercase Letter 13850374
41.8%
Dash Punctuation 16
 
< 0.1%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 1540391
 
11.1%
B 1153930
 
8.3%
F 1034684
 
7.5%
W 1021243
 
7.4%
M 897679
 
6.5%
T 825715
 
6.0%
V 731874
 
5.3%
A 726974
 
5.2%
X 593783
 
4.3%
C 533425
 
3.9%
Other values (16) 4790676
34.6%
Decimal Number
ValueCountFrequency (%)
0 3020728
15.7%
1 2597647
13.5%
2 2126799
11.0%
3 1973446
10.2%
6 1762506
9.1%
4 1733414
9.0%
5 1716567
8.9%
7 1556241
8.1%
8 1463087
7.6%
9 1322374
6.9%
Other Punctuation
ValueCountFrequency (%)
/ 5
55.6%
. 4
44.4%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19272834
58.2%
Latin 13850374
41.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 1540391
 
11.1%
B 1153930
 
8.3%
F 1034684
 
7.5%
W 1021243
 
7.4%
M 897679
 
6.5%
T 825715
 
6.0%
V 731874
 
5.3%
A 726974
 
5.2%
X 593783
 
4.3%
C 533425
 
3.9%
Other values (16) 4790676
34.6%
Common
ValueCountFrequency (%)
0 3020728
15.7%
1 2597647
13.5%
2 2126799
11.0%
3 1973446
10.2%
6 1762506
9.1%
4 1733414
9.0%
5 1716567
8.9%
7 1556241
8.1%
8 1463087
7.6%
9 1322374
6.9%
Other values (3) 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33123208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3020728
 
9.1%
1 2597647
 
7.8%
2 2126799
 
6.4%
3 1973446
 
6.0%
6 1762506
 
5.3%
4 1733414
 
5.2%
5 1716567
 
5.2%
7 1556241
 
4.7%
Z 1540391
 
4.7%
8 1463087
 
4.4%
Other values (29) 13632382
41.2%

Date
Date

Distinct337
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.7 MiB
Minimum2021-01-02 00:00:00
Maximum2021-12-31 00:00:00
2023-04-17T16:12:59.536656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:59.622773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MotorType
Categorical

Distinct32758
Distinct (%)1.7%
Missing5
Missing (%)< 0.1%
Memory size29.7 MiB
ALH
 
27532
BXE
 
24316
ASV
 
19456
781.136M
 
18251
BME
 
17862
Other values (32753)
1841002 

Length

Max length17
Median length16
Mean length4.5192061
Min length1

Characters and Unicode

Total characters8805307
Distinct characters88
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16127 ?
Unique (%)0.8%

Sample

1st rowF1AE3481D
2nd row60D/3
3rd row781.135N
4th row781.135
5th rowAR37203

Common Values

ValueCountFrequency (%)
ALH 27532
 
1.4%
BXE 24316
 
1.2%
ASV 19456
 
1.0%
781.136M 18251
 
0.9%
BME 17862
 
0.9%
AGR 17088
 
0.9%
AZQ 16615
 
0.9%
BLS 16512
 
0.8%
AQW 16218
 
0.8%
AWY 15913
 
0.8%
Other values (32748) 1758656
90.3%

Length

2023-04-17T16:12:59.710761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alh 27563
 
1.3%
bxe 24317
 
1.1%
7 20775
 
1.0%
asv 19490
 
0.9%
781.136m 18395
 
0.8%
bme 17872
 
0.8%
agr 17152
 
0.8%
azq 16622
 
0.8%
bls 16536
 
0.8%
aqw 16304
 
0.7%
Other values (24411) 1991744
91.1%

Most occurring characters

ValueCountFrequency (%)
A 778718
 
8.8%
B 532973
 
6.1%
1 529481
 
6.0%
F 457359
 
5.2%
4 394600
 
4.5%
D 386410
 
4.4%
0 361865
 
4.1%
C 311393
 
3.5%
6 282869
 
3.2%
H 265459
 
3.0%
Other values (78) 4504180
51.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5492430
62.4%
Decimal Number 2862304
32.5%
Space Separator 239111
 
2.7%
Other Punctuation 157827
 
1.8%
Dash Punctuation 49847
 
0.6%
Math Symbol 1363
 
< 0.1%
Lowercase Letter 894
 
< 0.1%
Open Punctuation 771
 
< 0.1%
Close Punctuation 760
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 778718
14.2%
B 532973
 
9.7%
F 457359
 
8.3%
D 386410
 
7.0%
C 311393
 
5.7%
H 265459
 
4.8%
E 265152
 
4.8%
M 254416
 
4.6%
K 209967
 
3.8%
X 198610
 
3.6%
Other values (26) 1831973
33.4%
Lowercase Letter
ValueCountFrequency (%)
a 99
 
11.1%
s 77
 
8.6%
c 56
 
6.3%
l 54
 
6.0%
f 53
 
5.9%
b 53
 
5.9%
e 51
 
5.7%
d 46
 
5.1%
m 45
 
5.0%
v 41
 
4.6%
Other values (18) 319
35.7%
Decimal Number
ValueCountFrequency (%)
1 529481
18.5%
4 394600
13.8%
0 361865
12.6%
6 282869
9.9%
2 260825
9.1%
8 233098
8.1%
7 232231
8.1%
3 232020
8.1%
9 203592
 
7.1%
5 131723
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 139631
88.5%
/ 12954
 
8.2%
, 2713
 
1.7%
* 2490
 
1.6%
? 31
 
< 0.1%
; 6
 
< 0.1%
: 2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 770
99.9%
{ 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 759
99.9%
} 1
 
0.1%
Space Separator
ValueCountFrequency (%)
239111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49847
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1363
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5493324
62.4%
Common 3311983
37.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 778718
14.2%
B 532973
 
9.7%
F 457359
 
8.3%
D 386410
 
7.0%
C 311393
 
5.7%
H 265459
 
4.8%
E 265152
 
4.8%
M 254416
 
4.6%
K 209967
 
3.8%
X 198610
 
3.6%
Other values (54) 1832867
33.4%
Common
ValueCountFrequency (%)
1 529481
16.0%
4 394600
11.9%
0 361865
10.9%
6 282869
8.5%
2 260825
7.9%
239111
7.2%
8 233098
7.0%
7 232231
7.0%
3 232020
7.0%
9 203592
 
6.1%
Other values (14) 342291
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8804407
> 99.9%
None 900
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 778718
 
8.8%
B 532973
 
6.1%
1 529481
 
6.0%
F 457359
 
5.2%
4 394600
 
4.5%
D 386410
 
4.4%
0 361865
 
4.1%
C 311393
 
3.5%
6 282869
 
3.2%
H 265459
 
3.0%
Other values (66) 4503280
51.1%
None
ValueCountFrequency (%)
Š 832
92.4%
Á 17
 
1.9%
Č 16
 
1.8%
š 9
 
1.0%
Ý 5
 
0.6%
Ž 5
 
0.6%
Ř 4
 
0.4%
Í 4
 
0.4%
Ě 3
 
0.3%
á 3
 
0.3%
Other values (2) 2
 
0.2%

Make
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct796
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.7 MiB
ŠKODA
551264 
FORD
164382 
VOLKSWAGEN
123551 
PEUGEOT
111096 
RENAULT
109273 
Other values (791)
888858 

Length

Max length28
Median length26
Mean length5.6478759
Min length2

Characters and Unicode

Total characters11004457
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique255 ?
Unique (%)< 0.1%

Sample

1st rowADRIA
2nd rowAEBI
3rd rowAGM
4th rowAGM
5th rowALFA ROMEO

Common Values

ValueCountFrequency (%)
ŠKODA 551264
28.3%
FORD 164382
 
8.4%
VOLKSWAGEN 123551
 
6.3%
PEUGEOT 111096
 
5.7%
RENAULT 109273
 
5.6%
VW 100199
 
5.1%
CITROËN 83570
 
4.3%
OPEL 66025
 
3.4%
MERCEDES-BENZ 58180
 
3.0%
FIAT 54227
 
2.8%
Other values (786) 526657
27.0%

Length

2023-04-17T16:12:59.798584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
škoda 551270
28.1%
ford 164382
 
8.4%
volkswagen 123551
 
6.3%
peugeot 111097
 
5.7%
renault 109312
 
5.6%
vw 100199
 
5.1%
citroën 83570
 
4.3%
opel 66025
 
3.4%
mercedes-benz 58183
 
3.0%
fiat 54230
 
2.8%
Other values (797) 541343
27.6%

Most occurring characters

ValueCountFrequency (%)
O 1327853
 
12.1%
A 1278651
 
11.6%
D 945097
 
8.6%
E 875871
 
8.0%
K 730138
 
6.6%
Š 551279
 
5.0%
N 530591
 
4.8%
T 513984
 
4.7%
R 486410
 
4.4%
I 419062
 
3.8%
Other values (57) 3345521
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10930650
99.3%
Dash Punctuation 58747
 
0.5%
Space Separator 14739
 
0.1%
Other Punctuation 253
 
< 0.1%
Lowercase Letter 32
 
< 0.1%
Decimal Number 31
 
< 0.1%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1327853
12.1%
A 1278651
 
11.7%
D 945097
 
8.6%
E 875871
 
8.0%
K 730138
 
6.7%
Š 551279
 
5.0%
N 530591
 
4.9%
T 513984
 
4.7%
R 486410
 
4.4%
I 419062
 
3.8%
Other values (27) 3271714
29.9%
Lowercase Letter
ValueCountFrequency (%)
o 7
21.9%
a 4
12.5%
l 4
12.5%
u 3
9.4%
s 3
9.4%
r 3
9.4%
i 1
 
3.1%
d 1
 
3.1%
h 1
 
3.1%
x 1
 
3.1%
Other values (4) 4
12.5%
Decimal Number
ValueCountFrequency (%)
0 9
29.0%
5 8
25.8%
1 3
 
9.7%
4 3
 
9.7%
2 2
 
6.5%
8 2
 
6.5%
6 1
 
3.2%
9 1
 
3.2%
7 1
 
3.2%
3 1
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 188
74.3%
/ 64
 
25.3%
, 1
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 58747
100.0%
Space Separator
ValueCountFrequency (%)
14739
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10930682
99.3%
Common 73775
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1327853
12.1%
A 1278651
 
11.7%
D 945097
 
8.6%
E 875871
 
8.0%
K 730138
 
6.7%
Š 551279
 
5.0%
N 530591
 
4.9%
T 513984
 
4.7%
R 486410
 
4.4%
I 419062
 
3.8%
Other values (41) 3271746
29.9%
Common
ValueCountFrequency (%)
- 58747
79.6%
14739
 
20.0%
. 188
 
0.3%
/ 64
 
0.1%
0 9
 
< 0.1%
5 8
 
< 0.1%
+ 5
 
< 0.1%
1 3
 
< 0.1%
4 3
 
< 0.1%
2 2
 
< 0.1%
Other values (6) 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10369464
94.2%
None 634993
 
5.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1327853
12.8%
A 1278651
12.3%
D 945097
 
9.1%
E 875871
 
8.4%
K 730138
 
7.0%
N 530591
 
5.1%
T 513984
 
5.0%
R 486410
 
4.7%
I 419062
 
4.0%
U 391273
 
3.8%
Other values (46) 2870534
27.7%
None
ValueCountFrequency (%)
Š 551279
86.8%
Ë 83570
 
13.2%
Ü 54
 
< 0.1%
Ö 27
 
< 0.1%
Č 24
 
< 0.1%
Á 21
 
< 0.1%
Ž 8
 
< 0.1%
Ě 7
 
< 0.1%
Í 1
 
< 0.1%
Ý 1
 
< 0.1%

VehicleType
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.7 MiB
OSOBNÍ AUTOMOBIL
1618863 
NÁKLADNÍ AUTOMOBIL
306122 
AUTOBUS
 
12845
MOTOCYKL
 
10594

Length

Max length18
Median length16
Mean length16.211395
Min length7

Characters and Unicode

Total characters31586671
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOSOBNÍ AUTOMOBIL
2nd rowNÁKLADNÍ AUTOMOBIL
3rd rowNÁKLADNÍ AUTOMOBIL
4th rowNÁKLADNÍ AUTOMOBIL
5th rowOSOBNÍ AUTOMOBIL

Common Values

ValueCountFrequency (%)
OSOBNÍ AUTOMOBIL 1618863
83.1%
NÁKLADNÍ AUTOMOBIL 306122
 
15.7%
AUTOBUS 12845
 
0.7%
MOTOCYKL 10594
 
0.5%

Length

2023-04-17T16:12:59.872280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T16:12:59.952197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
automobil 1924985
49.7%
osobní 1618863
41.8%
nákladní 306122
 
7.9%
autobus 12845
 
0.3%
motocykl 10594
 
0.3%

Most occurring characters

ValueCountFrequency (%)
O 7121729
22.5%
B 3556693
11.3%
A 2243952
 
7.1%
L 2241701
 
7.1%
N 2231107
 
7.1%
U 1950675
 
6.2%
T 1948424
 
6.2%
M 1935579
 
6.1%
1924985
 
6.1%
I 1924985
 
6.1%
Other values (7) 4506841
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 29661686
93.9%
Space Separator 1924985
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 7121729
24.0%
B 3556693
12.0%
A 2243952
 
7.6%
L 2241701
 
7.6%
N 2231107
 
7.5%
U 1950675
 
6.6%
T 1948424
 
6.6%
M 1935579
 
6.5%
I 1924985
 
6.5%
Í 1924985
 
6.5%
Other values (6) 2581856
 
8.7%
Space Separator
ValueCountFrequency (%)
1924985
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29661686
93.9%
Common 1924985
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 7121729
24.0%
B 3556693
12.0%
A 2243952
 
7.6%
L 2241701
 
7.6%
N 2231107
 
7.5%
U 1950675
 
6.6%
T 1948424
 
6.6%
M 1935579
 
6.5%
I 1924985
 
6.5%
Í 1924985
 
6.5%
Other values (6) 2581856
 
8.7%
Common
ValueCountFrequency (%)
1924985
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29355564
92.9%
None 2231107
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 7121729
24.3%
B 3556693
12.1%
A 2243952
 
7.6%
L 2241701
 
7.6%
N 2231107
 
7.6%
U 1950675
 
6.6%
T 1948424
 
6.6%
M 1935579
 
6.6%
1924985
 
6.6%
I 1924985
 
6.6%
Other values (5) 2275734
 
7.8%
None
ValueCountFrequency (%)
Í 1924985
86.3%
Á 306122
 
13.7%

Model
Categorical

Distinct15540
Distinct (%)0.8%
Missing137
Missing (%)< 0.1%
Memory size29.7 MiB
OCTAVIA
196407 
FABIA
158061 
GOLF
 
45535
FELICIA
 
43803
FOCUS
 
43258
Other values (15535)
1461223 

Length

Max length30
Median length28
Mean length6.2174156
Min length1

Characters and Unicode

Total characters12113310
Distinct characters80
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7476 ?
Unique (%)0.4%

Sample

1st rowMATRIX
2nd rowMT 740
3rd rowVARIANT
4th rowVARIANT
5th row147

Common Values

ValueCountFrequency (%)
OCTAVIA 196407
 
10.1%
FABIA 158061
 
8.1%
GOLF 45535
 
2.3%
FELICIA 43803
 
2.2%
FOCUS 43258
 
2.2%
FABIA COMBI 42793
 
2.2%
OCTAVIA COMBI 30068
 
1.5%
TRANSIT 26625
 
1.4%
PASSAT 26187
 
1.3%
206 25082
 
1.3%
Other values (15530) 1310468
67.3%

Length

2023-04-17T16:13:00.033090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
octavia 226492
 
9.8%
fabia 203794
 
8.9%
combi 81525
 
3.5%
golf 56523
 
2.5%
felicia 51707
 
2.2%
focus 48605
 
2.1%
passat 47123
 
2.0%
megane 37866
 
1.6%
transit 29690
 
1.3%
variant 27320
 
1.2%
Other values (9886) 1491787
64.8%

Most occurring characters

ValueCountFrequency (%)
A 1914199
15.8%
I 1029045
 
8.5%
O 940216
 
7.8%
T 772150
 
6.4%
C 760383
 
6.3%
R 649274
 
5.4%
E 583941
 
4.8%
S 562156
 
4.6%
F 468168
 
3.9%
N 434212
 
3.6%
Other values (70) 3999566
33.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10747097
88.7%
Decimal Number 987454
 
8.2%
Space Separator 354207
 
2.9%
Lowercase Letter 24552
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1914199
17.8%
I 1029045
 
9.6%
O 940216
 
8.7%
T 772150
 
7.2%
C 760383
 
7.1%
R 649274
 
6.0%
E 583941
 
5.4%
S 562156
 
5.2%
F 468168
 
4.4%
N 434212
 
4.0%
Other values (29) 2633353
24.5%
Lowercase Letter
ValueCountFrequency (%)
i 15494
63.1%
o 1619
 
6.6%
r 1543
 
6.3%
a 1503
 
6.1%
x 782
 
3.2%
e 664
 
2.7%
n 419
 
1.7%
d 415
 
1.7%
t 362
 
1.5%
s 324
 
1.3%
Other values (20) 1427
 
5.8%
Decimal Number
ValueCountFrequency (%)
0 253761
25.7%
3 131662
13.3%
2 124812
12.6%
1 94981
 
9.6%
5 88184
 
8.9%
6 84386
 
8.5%
4 84123
 
8.5%
7 60916
 
6.2%
8 47058
 
4.8%
9 17571
 
1.8%
Space Separator
ValueCountFrequency (%)
354207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10771649
88.9%
Common 1341661
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1914199
17.8%
I 1029045
 
9.6%
O 940216
 
8.7%
T 772150
 
7.2%
C 760383
 
7.1%
R 649274
 
6.0%
E 583941
 
5.4%
S 562156
 
5.2%
F 468168
 
4.3%
N 434212
 
4.0%
Other values (59) 2657905
24.7%
Common
ValueCountFrequency (%)
354207
26.4%
0 253761
18.9%
3 131662
 
9.8%
2 124812
 
9.3%
1 94981
 
7.1%
5 88184
 
6.6%
6 84386
 
6.3%
4 84123
 
6.3%
7 60916
 
4.5%
8 47058
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12112399
> 99.9%
None 911
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1914199
15.8%
I 1029045
 
8.5%
O 940216
 
7.8%
T 772150
 
6.4%
C 760383
 
6.3%
R 649274
 
5.4%
E 583941
 
4.8%
S 562156
 
4.6%
F 468168
 
3.9%
N 434212
 
3.6%
Other values (53) 3998655
33.0%
None
ValueCountFrequency (%)
É 448
49.2%
Á 369
40.5%
í 23
 
2.5%
á 17
 
1.9%
Ó 16
 
1.8%
Č 7
 
0.8%
é 7
 
0.8%
Ö 5
 
0.5%
Š 5
 
0.5%
Ý 4
 
0.4%
Other values (7) 10
 
1.1%

VehicleClass
Categorical

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.7 MiB
M1
1578631 
N1
190129 
N3
 
52702
M1G
 
40232
N2
 
35747
Other values (41)
 
50983

Length

Max length7
Median length2
Mean length2.0376217
Min length1

Characters and Unicode

Total characters3970151
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowM1
2nd rowN2G
3rd rowN1
4th rowN1
5th rowM1

Common Values

ValueCountFrequency (%)
M1 1578631
81.0%
N1 190129
 
9.8%
N3 52702
 
2.7%
M1G 40232
 
2.1%
N2 35747
 
1.8%
N1G 15969
 
0.8%
M3 11815
 
0.6%
N3G 10981
 
0.6%
LC 4217
 
0.2%
L3e 3006
 
0.2%
Other values (36) 4995
 
0.3%

Length

2023-04-17T16:13:00.112780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1 1578631
81.0%
n1 190129
 
9.8%
n3 52702
 
2.7%
m1g 40232
 
2.1%
n2 35747
 
1.8%
n1g 15969
 
0.8%
m3 11815
 
0.6%
n3g 10981
 
0.6%
lc 4217
 
0.2%
l3e 3006
 
0.2%
Other values (36) 4995
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 1825531
46.0%
M 1631711
41.1%
N 306122
 
7.7%
3 78985
 
2.0%
G 67774
 
1.7%
2 37464
 
0.9%
L 10592
 
0.3%
e 4325
 
0.1%
C 4218
 
0.1%
A 1597
 
< 0.1%
Other values (13) 1832
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2022880
51.0%
Decimal Number 1942545
48.9%
Lowercase Letter 4326
 
0.1%
Dash Punctuation 400
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1631711
80.7%
N 306122
 
15.1%
G 67774
 
3.4%
L 10592
 
0.5%
C 4218
 
0.2%
A 1597
 
0.1%
E 667
 
< 0.1%
B 184
 
< 0.1%
P 6
 
< 0.1%
S 3
 
< 0.1%
Other values (3) 6
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1825531
94.0%
3 78985
 
4.1%
2 37464
 
1.9%
7 407
 
< 0.1%
6 102
 
< 0.1%
5 48
 
< 0.1%
4 8
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 4325
> 99.9%
z 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2027206
51.1%
Common 1942945
48.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1631711
80.5%
N 306122
 
15.1%
G 67774
 
3.3%
L 10592
 
0.5%
e 4325
 
0.2%
C 4218
 
0.2%
A 1597
 
0.1%
E 667
 
< 0.1%
B 184
 
< 0.1%
P 6
 
< 0.1%
Other values (5) 10
 
< 0.1%
Common
ValueCountFrequency (%)
1 1825531
94.0%
3 78985
 
4.1%
2 37464
 
1.9%
7 407
 
< 0.1%
- 400
 
< 0.1%
6 102
 
< 0.1%
5 48
 
< 0.1%
4 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3970151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1825531
46.0%
M 1631711
41.1%
N 306122
 
7.7%
3 78985
 
2.0%
G 67774
 
1.7%
2 37464
 
0.9%
L 10592
 
0.3%
e 4325
 
0.1%
C 4218
 
0.1%
A 1597
 
< 0.1%
Other values (13) 1832
 
< 0.1%
Distinct12359
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size29.7 MiB
Minimum1952-01-01 00:00:00
Maximum2021-12-22 00:00:00
2023-04-17T16:13:00.194513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:00.282013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Km
Real number (ℝ)

Distinct461065
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215859.73
Minimum0
Maximum1000000
Zeros2118
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:00.369790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61679.15
Q1137196
median199191
Q3268777
95-th percentile417291.55
Maximum1000000
Range1000000
Interquartile range (IQR)131581

Descriptive statistics

Standard deviation122558.81
Coefficient of variation (CV)0.5677706
Kurtosis6.5115942
Mean215859.73
Median Absolute Deviation (MAD)65413
Skewness1.8324319
Sum4.2058628 × 1011
Variance1.5020661 × 1010
MonotonicityNot monotonic
2023-04-17T16:13:00.459777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2118
 
0.1%
399999 72
 
< 0.1%
999999 40
 
< 0.1%
5 36
 
< 0.1%
1 34
 
< 0.1%
3 33
 
< 0.1%
11 33
 
< 0.1%
6 33
 
< 0.1%
10 32
 
< 0.1%
4 31
 
< 0.1%
Other values (461055) 1945962
99.9%
ValueCountFrequency (%)
0 2118
0.1%
1 34
 
< 0.1%
2 21
 
< 0.1%
3 33
 
< 0.1%
4 31
 
< 0.1%
5 36
 
< 0.1%
6 33
 
< 0.1%
7 23
 
< 0.1%
8 19
 
< 0.1%
9 29
 
< 0.1%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
999999 40
< 0.1%
999978 1
 
< 0.1%
999933 1
 
< 0.1%
999924 1
 
< 0.1%
999901 1
 
< 0.1%
999872 1
 
< 0.1%
999800 1
 
< 0.1%
999753 1
 
< 0.1%
999730 1
 
< 0.1%

Result
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.7 MiB
způsobilé
1731933 
částečně způsobilé
193708 
nezpůsobilé
 
22783

Length

Max length18
Median length9
Mean length9.9181462
Min length9

Characters and Unicode

Total characters19324754
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowčástečně způsobilé
2nd rowzpůsobilé
3rd rowzpůsobilé
4th rowzpůsobilé
5th rowzpůsobilé

Common Values

ValueCountFrequency (%)
způsobilé 1731933
88.9%
částečně způsobilé 193708
 
9.9%
nezpůsobilé 22783
 
1.2%

Length

2023-04-17T16:13:00.542753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T16:13:00.628112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
způsobilé 1925641
89.9%
částečně 193708
 
9.0%
nezpůsobilé 22783
 
1.1%

Most occurring characters

ValueCountFrequency (%)
s 2142132
11.1%
z 1948424
10.1%
p 1948424
10.1%
ů 1948424
10.1%
o 1948424
10.1%
b 1948424
10.1%
i 1948424
10.1%
l 1948424
10.1%
é 1948424
10.1%
č 387416
 
2.0%
Other values (6) 1207814
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19131046
99.0%
Space Separator 193708
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 2142132
11.2%
z 1948424
10.2%
p 1948424
10.2%
ů 1948424
10.2%
o 1948424
10.2%
b 1948424
10.2%
i 1948424
10.2%
l 1948424
10.2%
é 1948424
10.2%
č 387416
 
2.0%
Other values (5) 1014106
5.3%
Space Separator
ValueCountFrequency (%)
193708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19131046
99.0%
Common 193708
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 2142132
11.2%
z 1948424
10.2%
p 1948424
10.2%
ů 1948424
10.2%
o 1948424
10.2%
b 1948424
10.2%
i 1948424
10.2%
l 1948424
10.2%
é 1948424
10.2%
č 387416
 
2.0%
Other values (5) 1014106
5.3%
Common
ValueCountFrequency (%)
193708
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14653074
75.8%
None 4671680
 
24.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 2142132
14.6%
z 1948424
13.3%
p 1948424
13.3%
o 1948424
13.3%
b 1948424
13.3%
i 1948424
13.3%
l 1948424
13.3%
e 216491
 
1.5%
n 216491
 
1.5%
t 193708
 
1.3%
None
ValueCountFrequency (%)
ů 1948424
41.7%
é 1948424
41.7%
č 387416
 
8.3%
á 193708
 
4.1%
ě 193708
 
4.1%

Weekday
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9315642
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:00.688988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3826633
Coefficient of variation (CV)0.47164693
Kurtosis-1.1336092
Mean2.9315642
Median Absolute Deviation (MAD)1
Skewness0.093111352
Sum5711930
Variance1.9117577
MonotonicityNot monotonic
2023-04-17T16:13:00.744627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 427878
22.0%
4 405964
20.8%
2 404585
20.8%
1 392844
20.2%
5 300590
15.4%
6 16465
 
0.8%
7 98
 
< 0.1%
ValueCountFrequency (%)
1 392844
20.2%
2 404585
20.8%
3 427878
22.0%
4 405964
20.8%
5 300590
15.4%
6 16465
 
0.8%
7 98
 
< 0.1%
ValueCountFrequency (%)
7 98
 
< 0.1%
6 16465
 
0.8%
5 300590
15.4%
4 405964
20.8%
3 427878
22.0%
2 404585
20.8%
1 392844
20.2%

DefectsA
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4227232
Minimum0
Maximum38
Zeros63395
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:00.818676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum38
Range38
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3294359
Coefficient of variation (CV)0.6805797
Kurtosis1.9115765
Mean3.4227232
Median Absolute Deviation (MAD)2
Skewness1.0980777
Sum6668916
Variance5.4262717
MonotonicityNot monotonic
2023-04-17T16:13:00.891182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 392443
20.1%
2 353436
18.1%
3 319433
16.4%
4 268713
13.8%
5 223089
11.4%
6 130930
 
6.7%
7 81736
 
4.2%
0 63395
 
3.3%
8 49903
 
2.6%
9 27792
 
1.4%
Other values (23) 37554
 
1.9%
ValueCountFrequency (%)
0 63395
 
3.3%
1 392443
20.1%
2 353436
18.1%
3 319433
16.4%
4 268713
13.8%
5 223089
11.4%
6 130930
 
6.7%
7 81736
 
4.2%
8 49903
 
2.6%
9 27792
 
1.4%
ValueCountFrequency (%)
38 1
 
< 0.1%
35 1
 
< 0.1%
32 1
 
< 0.1%
29 1
 
< 0.1%
28 3
 
< 0.1%
27 2
 
< 0.1%
26 3
 
< 0.1%
25 11
< 0.1%
24 12
< 0.1%
23 15
< 0.1%

DefectsB
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26568704
Minimum0
Maximum42
Zeros1737482
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:00.969789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0004966
Coefficient of variation (CV)3.7656958
Kurtosis46.408239
Mean0.26568704
Median Absolute Deviation (MAD)0
Skewness5.7237497
Sum517671
Variance1.0009934
MonotonicityNot monotonic
2023-04-17T16:13:01.040898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 1737482
89.2%
1 91187
 
4.7%
2 45599
 
2.3%
3 29615
 
1.5%
4 17699
 
0.9%
5 10838
 
0.6%
6 6327
 
0.3%
7 3806
 
0.2%
8 2183
 
0.1%
9 1390
 
0.1%
Other values (18) 2298
 
0.1%
ValueCountFrequency (%)
0 1737482
89.2%
1 91187
 
4.7%
2 45599
 
2.3%
3 29615
 
1.5%
4 17699
 
0.9%
5 10838
 
0.6%
6 6327
 
0.3%
7 3806
 
0.2%
8 2183
 
0.1%
9 1390
 
0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
32 1
 
< 0.1%
28 1
 
< 0.1%
26 3
 
< 0.1%
23 4
 
< 0.1%
22 4
 
< 0.1%
21 6
 
< 0.1%
20 9
 
< 0.1%
19 11
 
< 0.1%
18 33
< 0.1%

DefectsC
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014533798
Minimum0
Maximum12
Zeros1927545
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:01.102679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15800325
Coefficient of variation (CV)10.871436
Kurtosis317.45209
Mean0.014533798
Median Absolute Deviation (MAD)0
Skewness14.96257
Sum28318
Variance0.024965027
MonotonicityNot monotonic
2023-04-17T16:13:01.167398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1927545
98.9%
1 15462
 
0.8%
2 3978
 
0.2%
3 1039
 
0.1%
4 291
 
< 0.1%
5 67
 
< 0.1%
6 27
 
< 0.1%
7 8
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
ValueCountFrequency (%)
0 1927545
98.9%
1 15462
 
0.8%
2 3978
 
0.2%
3 1039
 
0.1%
4 291
 
< 0.1%
5 67
 
< 0.1%
6 27
 
< 0.1%
7 8
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
8 2
 
< 0.1%
7 8
 
< 0.1%
6 27
 
< 0.1%
5 67
 
< 0.1%
4 291
 
< 0.1%
3 1039
 
0.1%
2 3978
0.2%

Defects0
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12029466
Minimum0
Maximum5
Zeros1727980
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:01.227216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34714793
Coefficient of variation (CV)2.8858133
Kurtosis8.1735147
Mean0.12029466
Median Absolute Deviation (MAD)0
Skewness2.8768061
Sum234385
Variance0.12051168
MonotonicityNot monotonic
2023-04-17T16:13:01.291183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1727980
88.7%
1 206843
 
10.6%
2 13287
 
0.7%
3 290
 
< 0.1%
4 22
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 1727980
88.7%
1 206843
 
10.6%
2 13287
 
0.7%
3 290
 
< 0.1%
4 22
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 22
 
< 0.1%
3 290
 
< 0.1%
2 13287
 
0.7%
1 206843
 
10.6%
0 1727980
88.7%

Defects1
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81343742
Minimum0
Maximum14
Zeros900069
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:01.349913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94899276
Coefficient of variation (CV)1.1666451
Kurtosis2.4462742
Mean0.81343742
Median Absolute Deviation (MAD)1
Skewness1.3230826
Sum1584921
Variance0.90058727
MonotonicityNot monotonic
2023-04-17T16:13:01.409254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 900069
46.2%
1 655651
33.7%
2 284391
 
14.6%
3 82007
 
4.2%
4 19940
 
1.0%
5 4444
 
0.2%
6 1280
 
0.1%
7 438
 
< 0.1%
8 127
 
< 0.1%
9 52
 
< 0.1%
Other values (3) 25
 
< 0.1%
ValueCountFrequency (%)
0 900069
46.2%
1 655651
33.7%
2 284391
 
14.6%
3 82007
 
4.2%
4 19940
 
1.0%
5 4444
 
0.2%
6 1280
 
0.1%
7 438
 
< 0.1%
8 127
 
< 0.1%
9 52
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
11 3
 
< 0.1%
10 21
 
< 0.1%
9 52
 
< 0.1%
8 127
 
< 0.1%
7 438
 
< 0.1%
6 1280
 
0.1%
5 4444
 
0.2%
4 19940
 
1.0%
3 82007
4.2%

Defects2
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16945901
Minimum0
Maximum6
Zeros1645046
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:01.475548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41301603
Coefficient of variation (CV)2.4372622
Kurtosis6.5798887
Mean0.16945901
Median Absolute Deviation (MAD)0
Skewness2.4851961
Sum330178
Variance0.17058224
MonotonicityNot monotonic
2023-04-17T16:13:01.532243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1645046
84.4%
1 278639
 
14.3%
2 22869
 
1.2%
3 1694
 
0.1%
4 164
 
< 0.1%
5 9
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
0 1645046
84.4%
1 278639
 
14.3%
2 22869
 
1.2%
3 1694
 
0.1%
4 164
 
< 0.1%
5 9
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5 9
 
< 0.1%
4 164
 
< 0.1%
3 1694
 
0.1%
2 22869
 
1.2%
1 278639
 
14.3%
0 1645046
84.4%

Defects3
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17741159
Minimum0
Maximum6
Zeros1647310
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:01.594380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44601501
Coefficient of variation (CV)2.5140128
Kurtosis9.5482397
Mean0.17741159
Median Absolute Deviation (MAD)0
Skewness2.8161655
Sum345673
Variance0.19892939
MonotonicityNot monotonic
2023-04-17T16:13:01.650329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1647310
84.5%
1 262722
 
13.5%
2 33006
 
1.7%
3 4712
 
0.2%
4 579
 
< 0.1%
5 83
 
< 0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
0 1647310
84.5%
1 262722
 
13.5%
2 33006
 
1.7%
3 4712
 
0.2%
4 579
 
< 0.1%
5 83
 
< 0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
6 12
 
< 0.1%
5 83
 
< 0.1%
4 579
 
< 0.1%
3 4712
 
0.2%
2 33006
 
1.7%
1 262722
 
13.5%
0 1647310
84.5%

Defects4
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58579395
Minimum0
Maximum15
Zeros1117227
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:01.716882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82245947
Coefficient of variation (CV)1.4040081
Kurtosis5.9392616
Mean0.58579395
Median Absolute Deviation (MAD)0
Skewness1.8416127
Sum1141375
Variance0.67643957
MonotonicityNot monotonic
2023-04-17T16:13:01.780746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1117227
57.3%
1 597680
30.7%
2 179169
 
9.2%
3 40068
 
2.1%
4 9397
 
0.5%
5 3030
 
0.2%
6 1107
 
0.1%
7 424
 
< 0.1%
8 193
 
< 0.1%
9 77
 
< 0.1%
Other values (6) 52
 
< 0.1%
ValueCountFrequency (%)
0 1117227
57.3%
1 597680
30.7%
2 179169
 
9.2%
3 40068
 
2.1%
4 9397
 
0.5%
5 3030
 
0.2%
6 1107
 
0.1%
7 424
 
< 0.1%
8 193
 
< 0.1%
9 77
 
< 0.1%
ValueCountFrequency (%)
15 4
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 4
 
< 0.1%
11 13
 
< 0.1%
10 29
 
< 0.1%
9 77
 
< 0.1%
8 193
 
< 0.1%
7 424
 
< 0.1%
6 1107
0.1%

Defects5
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34989304
Minimum0
Maximum10
Zeros1377189
Zeros (%)70.7%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:01.850303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60022392
Coefficient of variation (CV)1.7154497
Kurtosis4.4725148
Mean0.34989304
Median Absolute Deviation (MAD)0
Skewness1.8568079
Sum681740
Variance0.36026875
MonotonicityNot monotonic
2023-04-17T16:13:01.915151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1377189
70.7%
1 475662
 
24.4%
2 83633
 
4.3%
3 9657
 
0.5%
4 1739
 
0.1%
5 426
 
< 0.1%
6 89
 
< 0.1%
7 17
 
< 0.1%
8 8
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
0 1377189
70.7%
1 475662
 
24.4%
2 83633
 
4.3%
3 9657
 
0.5%
4 1739
 
0.1%
5 426
 
< 0.1%
6 89
 
< 0.1%
7 17
 
< 0.1%
8 8
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 2
 
< 0.1%
8 8
 
< 0.1%
7 17
 
< 0.1%
6 89
 
< 0.1%
5 426
 
< 0.1%
4 1739
 
0.1%
3 9657
 
0.5%
2 83633
 
4.3%
1 475662
24.4%

Defects6
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4462268
Minimum0
Maximum19
Zeros371575
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:01.983027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.129977
Coefficient of variation (CV)0.78132766
Kurtosis2.442489
Mean1.4462268
Median Absolute Deviation (MAD)1
Skewness1.0668241
Sum2817863
Variance1.276848
MonotonicityNot monotonic
2023-04-17T16:13:02.048029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 766415
39.3%
2 514072
26.4%
0 371575
19.1%
3 205209
 
10.5%
4 62827
 
3.2%
5 18982
 
1.0%
6 5977
 
0.3%
7 2194
 
0.1%
8 688
 
< 0.1%
9 292
 
< 0.1%
Other values (9) 193
 
< 0.1%
ValueCountFrequency (%)
0 371575
19.1%
1 766415
39.3%
2 514072
26.4%
3 205209
 
10.5%
4 62827
 
3.2%
5 18982
 
1.0%
6 5977
 
0.3%
7 2194
 
0.1%
8 688
 
< 0.1%
9 292
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 6
 
< 0.1%
13 9
 
< 0.1%
12 27
 
< 0.1%
11 40
 
< 0.1%
10 106
 
< 0.1%
9 292
< 0.1%

Defects7
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012045633
Minimum0
Maximum6
Zeros1926885
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:02.114309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11902948
Coefficient of variation (CV)9.8815462
Kurtosis166.65257
Mean0.012045633
Median Absolute Deviation (MAD)0
Skewness11.489004
Sum23470
Variance0.014168016
MonotonicityNot monotonic
2023-04-17T16:13:02.169859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1926885
98.9%
1 19833
 
1.0%
2 1528
 
0.1%
3 136
 
< 0.1%
4 38
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 1926885
98.9%
1 19833
 
1.0%
2 1528
 
0.1%
3 136
 
< 0.1%
4 38
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 3
 
< 0.1%
4 38
 
< 0.1%
3 136
 
< 0.1%
2 1528
 
0.1%
1 19833
 
1.0%
0 1926885
98.9%

Defects8
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026936642
Minimum0
Maximum8
Zeros1911897
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:02.233064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21683079
Coefficient of variation (CV)8.0496591
Kurtosis120.56155
Mean0.026936642
Median Absolute Deviation (MAD)0
Skewness9.9804088
Sum52484
Variance0.04701559
MonotonicityNot monotonic
2023-04-17T16:13:02.294906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1911897
98.1%
1 24103
 
1.2%
2 9528
 
0.5%
3 2372
 
0.1%
4 433
 
< 0.1%
5 74
 
< 0.1%
6 13
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 1911897
98.1%
1 24103
 
1.2%
2 9528
 
0.5%
3 2372
 
0.1%
4 433
 
< 0.1%
5 74
 
< 0.1%
6 13
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 3
 
< 0.1%
6 13
 
< 0.1%
5 74
 
< 0.1%
4 433
 
< 0.1%
3 2372
 
0.1%
2 9528
 
0.5%
1 24103
 
1.2%
0 1911897
98.1%

Defects9
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0014745251
Minimum0
Maximum5
Zeros1946229
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:02.585522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.048338807
Coefficient of variation (CV)32.782629
Kurtosis2081.9097
Mean0.0014745251
Median Absolute Deviation (MAD)0
Skewness41.126237
Sum2873
Variance0.0023366403
MonotonicityNot monotonic
2023-04-17T16:13:02.644160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1946229
99.9%
1 1656
 
0.1%
2 423
 
< 0.1%
3 95
 
< 0.1%
4 19
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 1946229
99.9%
1 1656
 
0.1%
2 423
 
< 0.1%
3 95
 
< 0.1%
4 19
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 19
 
< 0.1%
3 95
 
< 0.1%
2 423
 
< 0.1%
1 1656
 
0.1%
0 1946229
99.9%

AgeDays
Real number (ℝ)

Distinct12359
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6231.8006
Minimum481
Maximum26039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 MiB
2023-04-17T16:13:02.720647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum481
5-th percentile2750
Q14966
median6293
Q37601
95-th percentile9320
Maximum26039
Range25558
Interquartile range (IQR)2635

Descriptive statistics

Standard deviation2015.162
Coefficient of variation (CV)0.32336753
Kurtosis0.38268742
Mean6231.8006
Median Absolute Deviation (MAD)1318
Skewness0.080220033
Sum1.214219 × 1010
Variance4060877.7
MonotonicityNot monotonic
2023-04-17T16:13:02.811164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8872 3771
 
0.2%
9237 3586
 
0.2%
8507 3490
 
0.2%
9602 3259
 
0.2%
9968 2426
 
0.1%
8141 2352
 
0.1%
10333 1925
 
0.1%
10698 1418
 
0.1%
7411 1352
 
0.1%
11429 1327
 
0.1%
Other values (12349) 1923518
98.7%
ValueCountFrequency (%)
481 2
< 0.1%
482 2
< 0.1%
483 2
< 0.1%
487 1
 
< 0.1%
488 2
< 0.1%
490 1
 
< 0.1%
495 1
 
< 0.1%
497 2
< 0.1%
501 1
 
< 0.1%
502 3
< 0.1%
ValueCountFrequency (%)
26039 1
< 0.1%
25308 1
< 0.1%
25140 1
< 0.1%
25133 1
< 0.1%
24943 1
< 0.1%
24659 1
< 0.1%
24212 1
< 0.1%
24138 1
< 0.1%
23756 1
< 0.1%
23411 1
< 0.1%

Interactions

2023-04-17T16:12:46.016986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:01.923535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:04.951008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:07.853988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:10.836007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:13.679967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:16.505422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:19.344781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:22.387945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:25.294056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:28.276502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:31.325117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:34.630441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:37.741428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:40.561771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:43.269276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:46.193185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:02.120316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:05.124491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:08.038948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:11.010871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:13.856127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:16.683191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:19.532497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:22.567741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:25.490946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:28.458836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:31.504114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:34.826752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:37.910283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:40.735056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:43.435744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:46.380915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:02.308523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:05.308314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:08.218699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:11.191655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:14.038614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:16.861497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:19.728314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:22.751406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:25.673337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:28.652481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:31.680624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:35.014842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:38.085189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:40.902278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:43.614152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:46.566209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:02.497826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:05.503425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:08.406594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:11.361191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:14.220028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:17.039592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:19.918727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:22.939090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:25.867349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:28.847174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:31.860823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:35.204570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:38.278305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:41.077686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:43.785524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:46.741343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:02.677940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:05.683887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:08.588913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:11.528790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:14.382201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:17.210046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:20.103109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:23.113643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:26.049443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:29.037468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:32.033032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:35.390920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:38.483990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:41.240538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:43.946841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:46.929483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:02.864113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:05.864933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:08.782179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:11.704154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:14.554491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:17.378534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:20.303046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:23.293517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:26.245235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:29.232521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:32.213588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:35.579537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:38.664246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:41.408584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:44.121747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:47.110802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:03.049997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:06.042212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:08.967234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:11.888677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:14.729362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:17.553723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:20.496889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:23.470113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:26.422606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:29.412499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:32.385203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:35.780903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:38.839095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:41.573850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:44.294806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:47.530110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:03.242610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:06.227541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:09.162057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:12.072111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:14.909699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:17.737638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:20.688812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:23.645315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:26.625805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:29.600489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:32.568610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:35.974969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:39.019424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:41.749260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:44.467814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:47.714256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:03.443535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:06.413172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:09.350996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:12.249701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:15.088354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:17.913166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:20.878850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:23.824359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:26.796023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:29.820229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:33.166282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:36.166623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:39.187062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:41.918165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:44.651216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:47.910311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:03.631846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:06.593562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:09.536308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:12.430686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:15.260285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:18.091519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:21.065036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:24.006089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:26.982099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:30.003714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:33.356078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:36.357858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:39.363067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:42.088359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:44.831737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:48.097233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:03.830326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:06.780888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:09.728805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:12.613705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:15.435884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:18.269618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:21.259465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:24.188736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:27.166134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:30.197032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:33.533476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:36.554744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:39.544473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:42.258723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:45.005963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:48.274841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:04.013150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:06.957079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:09.914626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:12.792244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:15.616447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:18.446146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:21.452064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:24.367883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:27.339404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:30.382702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:33.710446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:36.755922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:39.718064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:42.421724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:45.170445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:48.456670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:04.204811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:07.137060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:10.103931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:12.974762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:15.794011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:18.627492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:21.646650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:24.558166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:27.526841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:30.576992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:33.898275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:36.976104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:39.880312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:42.596509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:45.340527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:48.642686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:04.392119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:07.312912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:10.290900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:13.150118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:15.969552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:18.806072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:21.830910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:24.739861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:27.707955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:30.764942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:34.068954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:37.173548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:40.048259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:42.761896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:45.508909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:48.829905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:04.582957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:07.492641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:10.480465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:13.334757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:16.149011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:18.989659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:22.026227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:24.934102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:27.901911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:30.962052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:34.245902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:37.375830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:40.226458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:42.928809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:45.672606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:49.000217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:04.776458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:07.665035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:10.664445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:13.510159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:16.322561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:19.164958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:22.212258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:25.118832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:28.092856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:31.148379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:34.428161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:37.569284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:40.396946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:43.096685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:12:45.838106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-17T16:12:50.065853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-17T16:12:52.623288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-17T16:12:57.246894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays
StationId
3836evidenčníZFA250000026174012021-01-20F1AE3481DADRIAOSOBNÍ AUTOMOBILMATRIXM12014-06-25133265částečně způsobilé301010000000003218
3512pravidelnáTAH31100EF11120502021-01-1860D/3AEBINÁKLADNÍ AUTOMOBILMT 740N2G2016-01-0724578způsobilé180004000130002657
3772pravidelnáTN9FV1Z00RAAM50322021-01-14781.135NAGMNÁKLADNÍ AUTOMOBILVARIANTN11994-01-0196979způsobilé4500012010100010698
3835pravidelnáTN9FS1Z00RAAM51222021-01-05781.135AGMNÁKLADNÍ AUTOMOBILVARIANTN11994-12-06133569způsobilé2400010000300010359
3820pravidelnáZAR937000050186672021-01-21AR37203ALFA ROMEOOSOBNÍ AUTOMOBIL147M12001-06-25160646způsobilé430001001010007966
3749pravidelnáZAR937000031143622021-01-21937A2000ALFA ROMEOOSOBNÍ AUTOMOBIL147M12002-05-30269007způsobilé430000001110007627
3828pravidelnáZAR937000033259602021-01-20AR32104ALFA ROMEOOSOBNÍ AUTOMOBIL147M12006-01-30151261způsobilé310000000100006286
3326pravidelnáZAR937000030471602021-01-20AR 37203ALFA ROMEOOSOBNÍ AUTOMOBIL147M12001-08-15187974způsobilé370011101030007915
3761pravidelnáZAR937000034547512021-01-19937A3000ALFA ROMEOOSOBNÍ AUTOMOBIL147M12009-04-27107948způsobilé230000200100005103
3755pravidelnáZAR937000030680872021-01-19AR32104ALFA ROMEOOSOBNÍ AUTOMOBIL147M12001-12-14119517částečně způsobilé276002042140007794
TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays
StationId
3521pravidelnáKMHNM81XP3U0696792021-12-31J3HYUNDAIOSOBNÍ AUTOMOBILTERRACANM1G2002-12-11230783způsobilé570002110120007432
3506pravidelnáWBA1E11020J1246132021-12-31N47D20CBMWOSOBNÍ AUTOMOBIL118 DM12013-05-15268703způsobilé520010100000003624
3415opakovanáTMBBE21Z8621263952021-12-31BKDŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12005-07-01282275způsobilé520001001000006499
3307pravidelnáVSSZZZ1MZ1B0234552021-12-31AUSSEATOSOBNÍ AUTOMOBILTOLEDOM12001-06-12166848částečně způsobilé522001001110007979
3126pravidelnáWVWZZZ3CZGE1130292021-12-31CRLBVOLKSWAGENOSOBNÍ AUTOMOBILPASSAT VARIANT 3CM12015-12-08186469způsobilé510000010000002687
3125pravidelnáKMHJN81DP7U7044512021-12-31G6BAHYUNDAINÁKLADNÍ AUTOMOBILTUCSONN12007-07-12325447způsobilé550002001020005758
3307opakovanáXLRTEH4300G1916862021-12-31MX-13 355H2DAFNÁKLADNÍ AUTOMOBILXF 480 FTN32018-01-30509603způsobilé520001000010001903
3837pravidelnáWF0XXXTTFXDD201412021-12-31DRFBFORDNÁKLADNÍ AUTOMOBILTRANSITN12013-12-30177799částečně způsobilé514001101110003395
3125pravidelnáTMBKS21Z0821041272021-12-31BXEŠKODANÁKLADNÍ AUTOMOBILOCTAVIAN12008-01-07232919částečně způsobilé532001002010105579
3125pravidelnáVF7VABHXHHZ0129152021-12-31BH01CITROËNOSOBNÍ AUTOMOBILJUMPYM12017-09-2740164způsobilé510000010000002028

Duplicate rows

Most frequently occurring

TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays# duplicates
0evidenčníJMB0NV460RJ0005212021-11-054M40MITSUBISHIOSOBNÍ AUTOMOBILPAJEROM11994-01-01214174částečně způsobilé50101000000000106982
1evidenčníKPAX61EESKP0574822021-11-23672960SSANGYONGNÁKLADNÍ AUTOMOBILMUSSON1G2019-06-0556222částečně způsobilé2010100000000014122
2evidenčníTMBEA6NJ8LZ0913332021-06-08CHYŠKODAOSOBNÍ AUTOMOBILFABIAM12020-03-0413087částečně způsobilé2010100000000011392
3evidenčníTMBJF73T0C90527322021-12-01CFGŠKODAOSOBNÍ AUTOMOBILSUPERBM12012-03-12248275částečně způsobilé3010100000000040532
4evidenčníTMBJR7NE8K00363042021-12-07DADŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12018-09-2438715částečně způsobilé2010100000000016662
5evidenčníTMBZZZ1U7W21271172021-11-08AGUŠKODAOSOBNÍ AUTOMOBILOCTAVIA COMBIM11998-08-12283227částečně způsobilé1010100000000090142
6evidenčníU5YH5F14ALL0581512021-12-16G4LDKIAOSOBNÍ AUTOMOBILCEEDM12021-08-063975částečně způsobilé401010000000006192
7evidenčníVF3YDUMFC127214082021-08-054H03PEUGEOTNÁKLADNÍ AUTOMOBILBOXERN12014-11-12184336částečně způsobilé4010100000000030782
8evidenčníVF6SHTF24810542072021-09-07YD25RENAULTNÁKLADNÍ AUTOMOBILMAXITYN12008-10-23253102částečně způsobilé2010100000000052892
9evidenčníW0L0TGF35W80826222021-12-14X18XE1OPELOSOBNÍ AUTOMOBILASTRA CARAVANM11998-07-02242591částečně způsobilé2010100000000090552